
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
from sklearn import preprocessing
import warnings
import numpy as np


from sklearn import model_selection
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import RandomizedSearchCV
from sklearn.metrics import classification_report, confusion_matrix, f1_score, accuracy_score, precision_score, recall_score, roc_auc_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from imblearn.pipeline import make_pipeline as imbalanced_make_pipeline
from imblearn.over_sampling import SMOTE
from sklearn.preprocessing import MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import GridSearchCV
import joblib
from time import time

warnings.simplefilter(action="ignore", category=FutureWarning)
warnings.simplefilter(action="ignore", category=Warning)

import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots

from random import randint

from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import f_classif


df = pd.read_csv(r'C:\Users\Admin\Desktop\Projects\BreastCancer\breast_cancer.csv')
df.head()


df.dtypes

Class          object
Age            object
Menopause      object
Tumor-size     object
inv-nodes      object
node-caps      object
deg-malig       int64
breast         object
breast-quad    object
irradiat       object
dtype: object


col_names = ['Class', 'Menopause', 'node-caps', 'breast', 'irradiat', 'Age', 'inv-nodes', 'Tumor-size']

for col in col_names:
    df[col] = df[col].astype('category', copy=False).cat.codes


for i, col in enumerate(['Age', 'Menopause', 'Tumor-size', 'inv-nodes', 'node-caps', 'deg-malig', 'breast', 'breast-quad','irradiat']):
    plt.figure(i)
    sns.catplot(x=col, y="Class", data=df, kind='point', aspect=2, palette='gist_rainbow')

<Figure size 432x288 with 0 Axes>


df.isnull().sum()

Class          0
Age            0
Menopause      0
Tumor-size     0
inv-nodes      0
node-caps      0
deg-malig      0
breast         0
breast-quad    0
irradiat       0
dtype: int64


df.describe()


features=df.columns


fig = make_subplots(rows=1, cols=2)
fig.add_trace(go.Box( y=df[features[1]]),row=1,col=1)
fig.add_trace(go.Histogram(x=df[features[1]]),row=1,col=2)
fig.update_layout(height=600, title_text="Boxplot and histogram for feature: "+str(features[1]))
fig.update_layout(showlegend=False)
fig.show()


fig = make_subplots(rows=1, cols=2)
fig.add_trace(go.Box( y=df[features[2]]),row=1,col=1)
fig.add_trace(go.Histogram(x=df[features[2]]),row=1,col=2)
fig.update_layout(height=600, title_text="Boxplot and histogram for feature: "+str(features[2]))
fig.update_layout(showlegend=False)
fig.show()


fig = make_subplots(rows=1, cols=2)
fig.add_trace(go.Box( y=df[features[3]]),row=1,col=1)
fig.add_trace(go.Histogram(x=df[features[3]]),row=1,col=2)
fig.update_layout(height=600, title_text="Boxplot and histogram for feature: "+str(features[3]))
fig.add_hline(y=df.groupby(by=features[1]).count()["Tumor-size"].mean(),row=1,col=2)
fig.update_layout(showlegend=False)
fig.show()


fig = make_subplots(rows=1, cols=2)
fig.add_trace(go.Box( y=df[features[4]]),row=1,col=1)
fig.add_trace(go.Histogram(x=df[features[4]]),row=1,col=2)
fig.update_layout(height=600, title_text="Boxplot and histogram for feature: "+str(features[4]))
fig.add_hline(y=df.groupby(by=features[1]).count()["inv-nodes"].mean(),row=1,col=2)
fig.update_layout(showlegend=False)
fig.show()


fig = make_subplots(rows=1, cols=2)
fig.add_trace(go.Box( y=df[features[5]]),row=1,col=1)
fig.add_trace(go.Histogram(x=df[features[5]]),row=1,col=2)
fig.update_layout(height=600, title_text="Boxplot and histogram for feature: "+str(features[5]))
fig.update_layout(showlegend=False)
fig.show()


fig = make_subplots(rows=1, cols=2)
fig.add_trace(go.Box( y=df[features[6]]),row=1,col=1)
fig.add_trace(go.Histogram(x=df[features[6]]),row=1,col=2)
fig.update_layout(height=600, title_text="Boxplot and histogram for feature: "+str(features[6]))
fig.update_layout(showlegend=False)
fig.show()


fig = make_subplots(rows=1, cols=2)
fig.add_trace(go.Box( y=df[features[7]]),row=1,col=1)
fig.add_trace(go.Histogram(x=df[features[7]]),row=1,col=2)
fig.update_layout(height=600, title_text="Boxplot and histogram for feature: "+str(features[7]))
fig.update_layout(showlegend=False)
fig.show()


fig = make_subplots(rows=1, cols=2)
fig.add_trace(go.Box( y=df[features[8]]),row=1,col=1)
fig.add_trace(go.Histogram(x=df[features[8]]),row=1,col=2)
fig.update_layout(height=600, title_text="Boxplot and histogram for feature: "+str(features[8]))
fig.update_layout(showlegend=False)
fig.show()


fig = make_subplots(rows=1, cols=2)
fig.add_trace(go.Box( y=df[features[9]]),row=1,col=1)
fig.add_trace(go.Histogram(x=df[features[9]]),row=1,col=2)
fig.update_layout(height=600, title_text="Boxplot and histogram for feature: "+str(features[9]))
fig.update_layout(showlegend=False)
fig.show()


df.to_csv ("cleaned_df.csv", index = False, header=True)


# creating one hot encoding of the categorical columns.
data = pd.get_dummies(df, columns =['breast-quad','Menopause', 'node-caps', 'breast', 'irradiat', 'Age', 'inv-nodes', 'Tumor-size'])


data.head()


Y = df['Class']


X = data


x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.25)


x_train.shape, y_train.shape

((214, 42), (214,))


x_test.shape, y_test.shape

((72, 42), (72,))


classifier = LogisticRegression(solver='lbfgs',random_state=0)


classifier.fit(x_train, y_train)

LogisticRegression(random_state=0)


LogisticRegression(C = 1.0, class_weight = None, dual = False, 
   fit_intercept=True, intercept_scaling=1, max_iter=100, 
   multi_class='warn', n_jobs=None, penalty='l2', random_state=0, 
   solver='lbfgs', tol=0.0001, verbose=0, warm_start=False)

LogisticRegression(multi_class='warn', random_state=0)


predicted_y = classifier.predict(x_test)


predicted_y

array([1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,
       1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,
       0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0,
       1, 1, 0, 0, 1, 0], dtype=int8)


for x in range(len(predicted_y)):
   if (predicted_y[x] == 1):
      print(x, end="\t")

0	2	4	8	11	13	17	22	26	31	32	39	42	45	49	50	57	60	62	66	67	70


print('Accuracy: {:.2f}'.format(classifier.score(x_test, y_test)))

Accuracy: 1.00


# creating a confusion matrix
cm = confusion_matrix(y_test, predicted_y)

# printing the confusion matrix
plt.rcParams['figure.figsize'] = (12, 12)
sns.heatmap(cm, annot = True, cmap = 'gist_rainbow')
plt.title('Logistic Regression Confusion Matrix', fontweight = 25, fontsize = 18)
plt.show()


from sklearn import metrics
auc = metrics.roc_auc_score(y_test, predicted_y)

false_positive_rate, true_positive_rate, thresolds = metrics.roc_curve(y_test, predicted_y)

plt.figure(figsize=(10, 8), dpi=100)
plt.axis('scaled')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.title("AUC & ROC Curve")
plt.plot(false_positive_rate, true_positive_rate, 'g')
plt.fill_between(false_positive_rate, true_positive_rate, facecolor='lightgreen', alpha=0.7)
plt.text(0.95, 0.05, 'AUC = %0.4f' % auc, ha='right', fontsize=12, weight='bold', color='blue')
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.show()


from sklearn.metrics import precision_recall_curve

#calculate precision and recall
precision, recall, thresholds = precision_recall_curve(y_test, predicted_y)

#create precision recall curve
fig, ax = plt.subplots()
ax.plot(recall, precision, color='purple')

#add axis labels to plot
ax.set_title('Precision-Recall Curve')
ax.set_ylabel('Precision')
ax.set_xlabel('Recall')

#display plot
plt.show()


from sklearn.metrics import classification_report

target_names = ['y_test', 'predicted_y'] # target values

# Print classification report after a train/test split:
print(classification_report(y_test, predicted_y, target_names=target_names))

              precision    recall  f1-score   support

      y_test       1.00      1.00      1.00        50
 predicted_y       1.00      1.00      1.00        22

    accuracy                           1.00        72
   macro avg       1.00      1.00      1.00        72
weighted avg       1.00      1.00      1.00        72

	Class	Age	Menopause	Tumor-size	inv-nodes	node-caps	deg-malig	breast	breast-quad	irradiat
0	no-recurrence-events	30-39	premeno	30-34	0-2	no	3	left	left_low	no
1	no-recurrence-events	40-49	premeno	20-24	0-2	no	2	right	right_up	no
2	no-recurrence-events	40-49	premeno	20-24	0-2	no	2	left	left_low	no
3	no-recurrence-events	60-69	ge40	15-19	0-2	no	2	right	left_up	no
4	no-recurrence-events	40-49	premeno	0-4	0-2	no	2	right	right_low	no

	Class	Age	Menopause	Tumor-size	inv-nodes	node-caps	deg-malig	breast	irradiat
count	286.000000	286.000000	286.000000	286.000000	286.000000	286.000000	286.000000	286.000000	286.000000
mean	0.297203	2.664336	1.073427	4.062937	1.073427	1.167832	2.048951	0.468531	0.237762
std	0.457828	1.011818	0.986680	2.151187	1.935321	0.443052	0.738217	0.499883	0.426459
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000
25%	0.000000	2.000000	0.000000	3.000000	0.000000	1.000000	2.000000	0.000000	0.000000
50%	0.000000	3.000000	2.000000	4.000000	0.000000	1.000000	2.000000	0.000000	0.000000
75%	1.000000	3.000000	2.000000	5.000000	1.000000	1.000000	3.000000	1.000000	0.000000
max	1.000000	5.000000	2.000000	10.000000	6.000000	2.000000	3.000000	1.000000	1.000000

	deg-malig	breast-quad_left_low	breast-quad_left_up	breast-quad_right_low	breast-quad_right_up	Menopause_0	...	Tumor-size_2	Tumor-size_3	Tumor-size_5
0	3	1	0	0	0	0	...	0	0	1
1	2	0	0	0	1	0	...	0	1	0
2	2	1	0	0	0	0	...	0	1	0
3	2	0	1	0	0	1	...	1	0	0
4	2	0	0	1	0	0	...	0	0	0

Table of Contents

Breast Cancer Prediction with Logistic Regression¶

Import Dependancies¶

Read in Data¶

Exploritory Data Analysis¶

Export Cleaned Data set¶

One Hot Encoding¶

Develope Model¶

Substantiate Model¶

Examine Accuracy¶

Classification Report¶

	deg-malig	breast-quad_left_low	breast-quad_left_up	breast-quad_right_low	breast-quad_right_up	Menopause_0	...	Tumor-size_2	Tumor-size_3	Tumor-size_5
0	3	1	0	0	0	0	...	0	0	1
1	2	0	0	0	1	0	...	0	1	0
2	2	1	0	0	0	0	...	0	1	0
3	2	0	1	0	0	1	...	1	0	0
4	2	0	0	1	0	0	...	0	0	0

	deg-malig	breast-quad_left_low	breast-quad_left_up	breast-quad_right_low	breast-quad_right_up	Menopause_0	...	Tumor-size_2	Tumor-size_3	Tumor-size_5
0	3	1	0	0	0	0	...	0	0	1
1	2	0	0	0	1	0	...	0	1	0
2	2	1	0	0	0	0	...	0	1	0
3	2	0	1	0	0	1	...	1	0	0
4	2	0	0	1	0	0	...	0	0	0

	deg-malig	breast-quad_left_low	breast-quad_left_up	breast-quad_right_low	breast-quad_right_up	Menopause_0	...	Tumor-size_2	Tumor-size_3	Tumor-size_5
0	3	1	0	0	0	0	...	0	0	1
1	2	0	0	0	1	0	...	0	1	0
2	2	1	0	0	0	0	...	0	1	0
3	2	0	1	0	0	1	...	1	0	0
4	2	0	0	1	0	0	...	0	0	0